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Machine Learning for Cellular Biosensing

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Machine Learning for Cellular Biosensing


Vito Paolo Pastore


Plankton is at the bottom of the food chain. Microscopic phytoplankton account for about 50% of all photosynthesis on Earth, corresponding to 50 billion tons of carbon each year, or about 125 billion tonnes of sugar. Thus, monitoring plankton is paramount to infer potential dangerous changes to the ecosystem. In this work we describe an application of machine learning to environmental sensing which uses plankton as biosensor. An important bottleneck of the adoption of state of the art machine learning tools into cell biology is the need for large high quality annotations. We introduce a pipeline to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. We propose a set of morphological features to establish the baseline for using plankton as a biosensor. Using an anomaly detection approach, we show that it is possible to detect deviation from the average space of features for plankton microorganisms, that we propose could be related to environmental threat or perturbations. Such an approach can open the way for the development of an automatic Artificial Intelligence (AI) based system for using plankton as biosensor.



2020-05-26 at 3:00 pm